{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CPrecNet data viewer\n",
    "\n",
    "This is data downloaded from the [Meteorological Data Open Portal site](https://data.kma.go.kr/data/rmt/rmtList.do?code=11&pgmNo=62) and converted from Korean radar composite field data.\n",
    "\n",
    "This is data from 2020 to 2023, and only data with 22 consecutive sequences at 5-minute resolution was included.\n",
    "\n",
    "The data was converted using the marshar palmer equation, and normalized to -15 dBR and 30 dBR as the minimum and maximum values, respectively.\n",
    "\n",
    "The file format is numpy's NPZ format.\n",
    "\n",
    "The key in npz is in 'yyyymmddHHMM' format(yyyy is the 4-digit year, mm is the 2-digit month, dd is the 2-digit day, HH is the 2-digit hour, and MM is the 2-digit minute).\n",
    "\n",
    "If there are no 22 sequences or no precipitation data, the key value does not exist.\n",
    "\n",
    "Each key contains (256,256) data with 500m resolution.\n",
    "\n",
    "For detailed details, please refer to the paper mentioned as a reference.\n",
    "\n",
    "I wrote it with the idea of ​​placing 00-data-viewer.ipynb at the top, putting the npz data in the data folder as shown below, and running the code.\n",
    "\n",
    "```\n",
    ".\n",
    "|-- 00-data-viewer.ipynb\n",
    "|-- data\n",
    "|   |-- *.npz\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "The python version used was 3.8.8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brief data information\n",
    "\n",
    "A brief description of the files is shown in the table below.<br/>\n",
    "The file naming rules are as follows.<br/>\n",
    "<br/>\n",
    "|file name | discription|\n",
    "|-------|------|\n",
    "|hsr_22_AREA_YEAR_MONTH.npz|data of target area|\n",
    "|hsr_22_AREA_YEAR_MONTH_cond.npz|data of condition|\n",
    "\n",
    "\n",
    "AREA indicates area name (R1,R2,R3,R4,R5,R6,R7,R8,R9,R10,SEOUL,SEOUL_NON_UNI)<br/>\n",
    "YEAR indicates year from 2020 to 2013.<br/>\n",
    "From R6 to R10, there is only data until 2022.<br/>\n",
    "There are two types of MONTH: '3-8' and '1-2_9-12'. This refers to data from March to August (3-8) and data from September to February (1-2_9-12).<br/>\n",
    "'_cond' is condition data <br/>\n",
    "Anything without '_cond' is data in the target area. <br/>\n",
    "The conditions from R1 to R10 are non uniform conditions.<br/>\n",
    "The condition of SEOUL is a uniform condition. <br/>\n",
    "The condition of SEOUL_NON_UNI is a non uniform condition.<br/>\n",
    "\n",
    "Since hsr_22_SEOUL_YEAR_MONTH and hsr_22_SEOUL_NON_UNI_YEAR_MONTH overlap with the same data, only hsr_22_SEOUL_NON_UNI_YEAR_MONTH was uploaded.<br/>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View downloaded files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MINV_DBR = -15\n",
    "MAXV_DBR = 30\n",
    "\n",
    "d = np.load('./data/hsr_22_SEOUL_2020_1-2_9-12.npz')\n",
    "date = list(d.keys())[3860]\n",
    "d = d[date]\n",
    "\n",
    "fig,ax = plt.subplots(1,1,figsize=(4,3))\n",
    "\n",
    "ax.set_title('%s data'%date)\n",
    "cb = ax.contourf(np.arange(256)*0.5,np.arange(256)*0.5,d)\n",
    "t = plt.colorbar(cb)\n",
    "ticks = t.get_ticks()\n",
    "ticks = ['%.2f'%(t*(MAXV_DBR-MINV_DBR)+MINV_DBR) for t in ticks]\n",
    "t.set_ticklabels(ticks)\n",
    "t.set_label('dBR')\n",
    "\n",
    "ax.set_xlabel('[km]')\n",
    "ax.set_ylabel('[km]')\n",
    "ax.tick_params(axis=\"both\", direction='in')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "precipitation",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
